research_sweep.py
"""
Broadcast Mode for Parallel Research Sweep
Demonstrates broadcast mode for gathering information from multiple sources
simultaneously. Each agent specializes in a different source, and the leader
merges findings into a comprehensive report.
"""
from agno.agent import Agent
from agno.models.openai import OpenAIResponses
from agno.team.mode import TeamMode
from agno.team.team import Team
from agno.tools.duckduckgo import DuckDuckGoTools
from agno.tools.hackernews import HackerNewsTools
# ---------------------------------------------------------------------------
# Create Members
# ---------------------------------------------------------------------------
web_researcher = Agent(
name="Web Researcher",
role="Searches the general web for information",
model=OpenAIResponses(id="gpt-5.2"),
tools=[DuckDuckGoTools()],
instructions=[
"Search the web for the given topic.",
"Focus on recent, authoritative sources.",
"Provide a concise summary of key findings.",
],
)
hn_researcher = Agent(
name="HackerNews Researcher",
role="Searches Hacker News for community discussions and stories",
model=OpenAIResponses(id="gpt-5.2"),
tools=[HackerNewsTools()],
instructions=[
"Search Hacker News for stories and discussions on the topic.",
"Highlight top-voted stories and notable community opinions.",
"Provide story titles, scores, and key takeaways.",
],
)
trend_analyst = Agent(
name="Trend Analyst",
role="Analyzes broader trends and implications from available data",
model=OpenAIResponses(id="gpt-5.2"),
instructions=[
"Analyze the topic from a trends perspective.",
"Identify patterns: is interest growing, plateauing, or declining?",
"Consider industry, academic, and public interest angles.",
],
)
# ---------------------------------------------------------------------------
# Create Team
# ---------------------------------------------------------------------------
team = Team(
name="Research Sweep Team",
mode=TeamMode.broadcast,
model=OpenAIResponses(id="gpt-5.2"),
members=[web_researcher, hn_researcher, trend_analyst],
instructions=[
"You lead a research sweep team.",
"All researchers investigate the same topic from different angles.",
"Merge their findings into a comprehensive report covering:",
"1. Key facts and recent developments",
"2. Community sentiment and notable discussions",
"3. Overall trend analysis and outlook",
],
show_members_responses=True,
markdown=True,
)
# ---------------------------------------------------------------------------
# Run Team
# ---------------------------------------------------------------------------
if __name__ == "__main__":
team.print_response(
"Research the current state of WebAssembly adoption in 2025.",
stream=True,
)
Run the Example
1
Set up your virtual environment
uv venv --python 3.12
source .venv/bin/activate
uv venv --python 3.12
.venv\Scripts\activate
2
Install dependencies
uv pip install -U agno ddgs openai
3
Export your OpenAI API key
export OPENAI_API_KEY="your_openai_api_key_here"
$Env:OPENAI_API_KEY="your_openai_api_key_here"
4
Run the example
Save the code above as
research_sweep.py, then run:python research_sweep.py